{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression\n",
    "\n",
    "Why use linear regression?\n",
    "\n",
    "1. Easy to use\n",
    "2. Easy to interpret\n",
    "3. Basis for many methods\n",
    "4. Runs fast\n",
    "5. Most people have heard about it :-) \n",
    "\n",
    "### Libraries in Python for Linear Regression\n",
    "\n",
    "The two most popular ones are\n",
    "\n",
    "1. `scikit-learn`\n",
    "2. `statsmodels`\n",
    "\n",
    "We highly recommend learning `scikit-learn` since that's also the machine learning package in Python.\n",
    "\n",
    "## Problem\n",
    "\n",
    "Could we predict price of weed in a state using the demographic information? \n",
    "\n",
    "For this session, let's do this. For January 2015, let's find average price for high quality weed across all the states. Let's assume that we don't know what the prices are for the following states: \n",
    "<br>\n",
    "iowa, kentucky, missouri, nevada, wyoming, south dakota, new jersey, michigan, idaho\n",
    "<br>\n",
    "\n",
    "Those are our **test** set. The remaining states are our **train** set. We need to train the model on the train dataset and predict for the test dataset. \n",
    "\n",
    "Since we also know the actual mean prices for the test states, let's verify how good our models are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Load the data\n",
    "weed_pd = pd.read_csv(\"../data/Weed_Price.csv\", parse_dates=[-1])\n",
    "demo_pd = pd.read_csv(\"../data/Demographics_State.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>HighQ</th>\n",
       "      <th>HighQN</th>\n",
       "      <th>MedQ</th>\n",
       "      <th>MedQN</th>\n",
       "      <th>LowQ</th>\n",
       "      <th>LowQN</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>339.06</td>\n",
       "      <td>1042</td>\n",
       "      <td>198.64</td>\n",
       "      <td>933</td>\n",
       "      <td>149.49</td>\n",
       "      <td>123</td>\n",
       "      <td>2014-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>288.75</td>\n",
       "      <td>252</td>\n",
       "      <td>260.60</td>\n",
       "      <td>297</td>\n",
       "      <td>388.58</td>\n",
       "      <td>26</td>\n",
       "      <td>2014-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>303.31</td>\n",
       "      <td>1941</td>\n",
       "      <td>209.35</td>\n",
       "      <td>1625</td>\n",
       "      <td>189.45</td>\n",
       "      <td>222</td>\n",
       "      <td>2014-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>361.85</td>\n",
       "      <td>576</td>\n",
       "      <td>185.62</td>\n",
       "      <td>544</td>\n",
       "      <td>125.87</td>\n",
       "      <td>112</td>\n",
       "      <td>2014-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>248.78</td>\n",
       "      <td>12096</td>\n",
       "      <td>193.56</td>\n",
       "      <td>12812</td>\n",
       "      <td>192.92</td>\n",
       "      <td>778</td>\n",
       "      <td>2014-01-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        State   HighQ  HighQN    MedQ  MedQN    LowQ  LowQN       date\n",
       "0     Alabama  339.06    1042  198.64    933  149.49    123 2014-01-01\n",
       "1      Alaska  288.75     252  260.60    297  388.58     26 2014-01-01\n",
       "2     Arizona  303.31    1941  209.35   1625  189.45    222 2014-01-01\n",
       "3    Arkansas  361.85     576  185.62    544  125.87    112 2014-01-01\n",
       "4  California  248.78   12096  193.56  12812  192.92    778 2014-01-01"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weed_pd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "State             object\n",
       "HighQ            float64\n",
       "HighQN             int64\n",
       "MedQ             float64\n",
       "MedQN              int64\n",
       "LowQ             float64\n",
       "LowQN              int64\n",
       "date      datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weed_pd.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>total_population</th>\n",
       "      <th>percent_white</th>\n",
       "      <th>percent_black</th>\n",
       "      <th>percent_asian</th>\n",
       "      <th>percent_hispanic</th>\n",
       "      <th>per_capita_income</th>\n",
       "      <th>median_rent</th>\n",
       "      <th>median_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>alabama</td>\n",
       "      <td>4799277</td>\n",
       "      <td>67</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>23680</td>\n",
       "      <td>501</td>\n",
       "      <td>38.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>alaska</td>\n",
       "      <td>720316</td>\n",
       "      <td>63</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>32651</td>\n",
       "      <td>978</td>\n",
       "      <td>33.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>arizona</td>\n",
       "      <td>6479703</td>\n",
       "      <td>57</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>25358</td>\n",
       "      <td>747</td>\n",
       "      <td>36.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>arkansas</td>\n",
       "      <td>2933369</td>\n",
       "      <td>74</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>22170</td>\n",
       "      <td>480</td>\n",
       "      <td>37.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>california</td>\n",
       "      <td>37659181</td>\n",
       "      <td>40</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>38</td>\n",
       "      <td>29527</td>\n",
       "      <td>1119</td>\n",
       "      <td>35.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       region  total_population  percent_white  percent_black  percent_asian  \\\n",
       "0     alabama           4799277             67             26              1   \n",
       "1      alaska            720316             63              3              5   \n",
       "2     arizona           6479703             57              4              3   \n",
       "3    arkansas           2933369             74             15              1   \n",
       "4  california          37659181             40              6             13   \n",
       "\n",
       "   percent_hispanic  per_capita_income  median_rent  median_age  \n",
       "0                 4              23680          501        38.1  \n",
       "1                 6              32651          978        33.6  \n",
       "2                30              25358          747        36.3  \n",
       "3                 7              22170          480        37.5  \n",
       "4                38              29527         1119        35.4  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demo_pd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "region                object\n",
       "total_population       int64\n",
       "percent_white          int64\n",
       "percent_black          int64\n",
       "percent_asian          int64\n",
       "percent_hispanic       int64\n",
       "per_capita_income      int64\n",
       "median_rent            int64\n",
       "median_age           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demo_pd.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As seen above, weed price dataset has states' first alphabet capitalized. \n",
    "The below command will convert it to lower case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'alabama'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str.lower(\"Alabama\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "?weed_pd.apply"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "weed_pd.State = weed_pd[\"State\"].apply(lambda x: str.lower(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>HighQ</th>\n",
       "      <th>HighQN</th>\n",
       "      <th>MedQ</th>\n",
       "      <th>MedQN</th>\n",
       "      <th>LowQ</th>\n",
       "      <th>LowQN</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>alabama</td>\n",
       "      <td>339.06</td>\n",
       "      <td>1042</td>\n",
       "      <td>198.64</td>\n",
       "      <td>933</td>\n",
       "      <td>149.49</td>\n",
       "      <td>123</td>\n",
       "      <td>2014-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>alaska</td>\n",
       "      <td>288.75</td>\n",
       "      <td>252</td>\n",
       "      <td>260.60</td>\n",
       "      <td>297</td>\n",
       "      <td>388.58</td>\n",
       "      <td>26</td>\n",
       "      <td>2014-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>arizona</td>\n",
       "      <td>303.31</td>\n",
       "      <td>1941</td>\n",
       "      <td>209.35</td>\n",
       "      <td>1625</td>\n",
       "      <td>189.45</td>\n",
       "      <td>222</td>\n",
       "      <td>2014-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>arkansas</td>\n",
       "      <td>361.85</td>\n",
       "      <td>576</td>\n",
       "      <td>185.62</td>\n",
       "      <td>544</td>\n",
       "      <td>125.87</td>\n",
       "      <td>112</td>\n",
       "      <td>2014-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>california</td>\n",
       "      <td>248.78</td>\n",
       "      <td>12096</td>\n",
       "      <td>193.56</td>\n",
       "      <td>12812</td>\n",
       "      <td>192.92</td>\n",
       "      <td>778</td>\n",
       "      <td>2014-01-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        State   HighQ  HighQN    MedQ  MedQN    LowQ  LowQN       date\n",
       "0     alabama  339.06    1042  198.64    933  149.49    123 2014-01-01\n",
       "1      alaska  288.75     252  260.60    297  388.58     26 2014-01-01\n",
       "2     arizona  303.31    1941  209.35   1625  189.45    222 2014-01-01\n",
       "3    arkansas  361.85     576  185.62    544  125.87    112 2014-01-01\n",
       "4  california  248.78   12096  193.56  12812  192.92    778 2014-01-01"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weed_pd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['alabama', 'alaska', 'arizona', 'arkansas', 'california',\n",
       "       'colorado', 'connecticut', 'delaware', 'district of columbia',\n",
       "       'florida', 'georgia', 'hawaii', 'idaho', 'illinois', 'indiana',\n",
       "       'iowa', 'kansas', 'kentucky', 'louisiana', 'maine', 'montana',\n",
       "       'nebraska', 'nevada', 'new hampshire', 'new jersey', 'new mexico',\n",
       "       'new york', 'north carolina', 'north dakota', 'ohio', 'oklahoma',\n",
       "       'oregon', 'maryland', 'massachusetts', 'michigan', 'minnesota',\n",
       "       'mississippi', 'missouri', 'pennsylvania', 'rhode island',\n",
       "       'south carolina', 'south dakota', 'tennessee', 'texas', 'utah',\n",
       "       'vermont', 'virginia', 'washington', 'west virginia', 'wisconsin',\n",
       "       'wyoming'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.unique(weed_pd.State)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['alabama', 'alaska', 'arizona', 'arkansas', 'california',\n",
       "       'colorado', 'connecticut', 'delaware', 'district of columbia',\n",
       "       'florida', 'georgia', 'hawaii', 'idaho', 'illinois', 'indiana',\n",
       "       'iowa', 'kansas', 'kentucky', 'louisiana', 'maine', 'maryland',\n",
       "       'massachusetts', 'michigan', 'minnesota', 'mississippi', 'missouri',\n",
       "       'montana', 'nebraska', 'nevada', 'new hampshire', 'new jersey',\n",
       "       'new mexico', 'new york', 'north carolina', 'north dakota', 'ohio',\n",
       "       'oklahoma', 'oregon', 'pennsylvania', 'rhode island',\n",
       "       'south carolina', 'south dakota', 'tennessee', 'texas', 'utah',\n",
       "       'vermont', 'virginia', 'washington', 'west virginia', 'wisconsin',\n",
       "       'wyoming'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.unique(demo_pd.region)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Let's get month and year of the date, so that we can select only Jan 2015 data\n",
    "weed_pd[\"Month\"] = weed_pd[\"date\"].apply(lambda x: x.month)\n",
    "weed_pd[\"Year\"] = weed_pd[\"date\"].apply(lambda x: x.year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weed_jan2015_pd = weed_pd.ix[(weed_pd.Year==2015) & (weed_pd.Month==1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>HighQ</th>\n",
       "      <th>HighQN</th>\n",
       "      <th>MedQ</th>\n",
       "      <th>MedQN</th>\n",
       "      <th>LowQ</th>\n",
       "      <th>LowQN</th>\n",
       "      <th>date</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>alabama</td>\n",
       "      <td>337.54</td>\n",
       "      <td>1539</td>\n",
       "      <td>208.24</td>\n",
       "      <td>1463</td>\n",
       "      <td>NaN</td>\n",
       "      <td>182</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>alaska</td>\n",
       "      <td>303.02</td>\n",
       "      <td>350</td>\n",
       "      <td>270.45</td>\n",
       "      <td>475</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>arizona</td>\n",
       "      <td>298.51</td>\n",
       "      <td>2638</td>\n",
       "      <td>209.43</td>\n",
       "      <td>2426</td>\n",
       "      <td>NaN</td>\n",
       "      <td>306</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>arkansas</td>\n",
       "      <td>341.68</td>\n",
       "      <td>846</td>\n",
       "      <td>207.20</td>\n",
       "      <td>836</td>\n",
       "      <td>NaN</td>\n",
       "      <td>145</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>california</td>\n",
       "      <td>243.96</td>\n",
       "      <td>16512</td>\n",
       "      <td>189.35</td>\n",
       "      <td>19151</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1096</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         State   HighQ  HighQN    MedQ  MedQN  LowQ  LowQN       date  Month  \\\n",
       "51     alabama  337.54    1539  208.24   1463   NaN    182 2015-01-01      1   \n",
       "52      alaska  303.02     350  270.45    475   NaN     37 2015-01-01      1   \n",
       "53     arizona  298.51    2638  209.43   2426   NaN    306 2015-01-01      1   \n",
       "54    arkansas  341.68     846  207.20    836   NaN    145 2015-01-01      1   \n",
       "55  california  243.96   16512  189.35  19151   NaN   1096 2015-01-01      1   \n",
       "\n",
       "    Year  \n",
       "51  2015  \n",
       "52  2015  \n",
       "53  2015  \n",
       "54  2015  \n",
       "55  2015  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weed_jan2015_pd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "weed_jan2015_summarized = weed_jan2015_pd[[\"State\", \"HighQ\"]].groupby(\"State\").mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>HighQ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>alabama</td>\n",
       "      <td>336.504839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>alaska</td>\n",
       "      <td>302.972581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>arizona</td>\n",
       "      <td>297.782903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>arkansas</td>\n",
       "      <td>341.705806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>california</td>\n",
       "      <td>243.602258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>colorado</td>\n",
       "      <td>243.273226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>connecticut</td>\n",
       "      <td>336.442258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>delaware</td>\n",
       "      <td>363.908387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>district of columbia</td>\n",
       "      <td>348.107097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>florida</td>\n",
       "      <td>300.604194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>georgia</td>\n",
       "      <td>323.562581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>hawaii</td>\n",
       "      <td>306.979677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>idaho</td>\n",
       "      <td>274.990323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>illinois</td>\n",
       "      <td>353.133226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>indiana</td>\n",
       "      <td>332.059677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>iowa</td>\n",
       "      <td>364.967742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>kansas</td>\n",
       "      <td>349.559032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>kentucky</td>\n",
       "      <td>339.014516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>louisiana</td>\n",
       "      <td>361.621290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>maine</td>\n",
       "      <td>310.723548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>maryland</td>\n",
       "      <td>364.671935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>massachusetts</td>\n",
       "      <td>344.880323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>michigan</td>\n",
       "      <td>287.620000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>minnesota</td>\n",
       "      <td>349.167097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>mississippi</td>\n",
       "      <td>336.288065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>missouri</td>\n",
       "      <td>351.059355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>montana</td>\n",
       "      <td>265.517097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>nebraska</td>\n",
       "      <td>335.264839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>nevada</td>\n",
       "      <td>265.610968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>new hampshire</td>\n",
       "      <td>353.643548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>new jersey</td>\n",
       "      <td>345.144839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>new mexico</td>\n",
       "      <td>288.175161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>new york</td>\n",
       "      <td>343.555161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>north carolina</td>\n",
       "      <td>344.903871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>north dakota</td>\n",
       "      <td>392.652581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>ohio</td>\n",
       "      <td>332.440323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>oklahoma</td>\n",
       "      <td>353.359677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>oregon</td>\n",
       "      <td>205.483226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>pennsylvania</td>\n",
       "      <td>362.544194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>rhode island</td>\n",
       "      <td>317.085806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>south carolina</td>\n",
       "      <td>337.197419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>south dakota</td>\n",
       "      <td>357.954516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>tennessee</td>\n",
       "      <td>347.931613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>texas</td>\n",
       "      <td>333.814194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>utah</td>\n",
       "      <td>289.862258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>vermont</td>\n",
       "      <td>370.526452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>virginia</td>\n",
       "      <td>364.769032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>washington</td>\n",
       "      <td>233.028387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>west virginia</td>\n",
       "      <td>358.037742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>wisconsin</td>\n",
       "      <td>349.942903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>wyoming</td>\n",
       "      <td>320.450968</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   State       HighQ\n",
       "0                alabama  336.504839\n",
       "1                 alaska  302.972581\n",
       "2                arizona  297.782903\n",
       "3               arkansas  341.705806\n",
       "4             california  243.602258\n",
       "5               colorado  243.273226\n",
       "6            connecticut  336.442258\n",
       "7               delaware  363.908387\n",
       "8   district of columbia  348.107097\n",
       "9                florida  300.604194\n",
       "10               georgia  323.562581\n",
       "11                hawaii  306.979677\n",
       "12                 idaho  274.990323\n",
       "13              illinois  353.133226\n",
       "14               indiana  332.059677\n",
       "15                  iowa  364.967742\n",
       "16                kansas  349.559032\n",
       "17              kentucky  339.014516\n",
       "18             louisiana  361.621290\n",
       "19                 maine  310.723548\n",
       "20              maryland  364.671935\n",
       "21         massachusetts  344.880323\n",
       "22              michigan  287.620000\n",
       "23             minnesota  349.167097\n",
       "24           mississippi  336.288065\n",
       "25              missouri  351.059355\n",
       "26               montana  265.517097\n",
       "27              nebraska  335.264839\n",
       "28                nevada  265.610968\n",
       "29         new hampshire  353.643548\n",
       "30            new jersey  345.144839\n",
       "31            new mexico  288.175161\n",
       "32              new york  343.555161\n",
       "33        north carolina  344.903871\n",
       "34          north dakota  392.652581\n",
       "35                  ohio  332.440323\n",
       "36              oklahoma  353.359677\n",
       "37                oregon  205.483226\n",
       "38          pennsylvania  362.544194\n",
       "39          rhode island  317.085806\n",
       "40        south carolina  337.197419\n",
       "41          south dakota  357.954516\n",
       "42             tennessee  347.931613\n",
       "43                 texas  333.814194\n",
       "44                  utah  289.862258\n",
       "45               vermont  370.526452\n",
       "46              virginia  364.769032\n",
       "47            washington  233.028387\n",
       "48         west virginia  358.037742\n",
       "49             wisconsin  349.942903\n",
       "50               wyoming  320.450968"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The source price dataset for our model \n",
    "weed_jan2015_summarized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_states = [\"iowa\", \"kentucky\", \"missouri\", \"nevada\", \"wyoming\", \\\n",
    "               \"south dakota\", \"new jersey\", \"michigan\", \"idaho\" ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data_for_model = pd.merge(weed_jan2015_summarized, demo_pd, left_on=\"State\", right_on=\"region\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>HighQ</th>\n",
       "      <th>region</th>\n",
       "      <th>total_population</th>\n",
       "      <th>percent_white</th>\n",
       "      <th>percent_black</th>\n",
       "      <th>percent_asian</th>\n",
       "      <th>percent_hispanic</th>\n",
       "      <th>per_capita_income</th>\n",
       "      <th>median_rent</th>\n",
       "      <th>median_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>alabama</td>\n",
       "      <td>336.504839</td>\n",
       "      <td>alabama</td>\n",
       "      <td>4799277</td>\n",
       "      <td>67</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>23680</td>\n",
       "      <td>501</td>\n",
       "      <td>38.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>alaska</td>\n",
       "      <td>302.972581</td>\n",
       "      <td>alaska</td>\n",
       "      <td>720316</td>\n",
       "      <td>63</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>32651</td>\n",
       "      <td>978</td>\n",
       "      <td>33.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>arizona</td>\n",
       "      <td>297.782903</td>\n",
       "      <td>arizona</td>\n",
       "      <td>6479703</td>\n",
       "      <td>57</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>25358</td>\n",
       "      <td>747</td>\n",
       "      <td>36.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>arkansas</td>\n",
       "      <td>341.705806</td>\n",
       "      <td>arkansas</td>\n",
       "      <td>2933369</td>\n",
       "      <td>74</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>22170</td>\n",
       "      <td>480</td>\n",
       "      <td>37.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>california</td>\n",
       "      <td>243.602258</td>\n",
       "      <td>california</td>\n",
       "      <td>37659181</td>\n",
       "      <td>40</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>38</td>\n",
       "      <td>29527</td>\n",
       "      <td>1119</td>\n",
       "      <td>35.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        State       HighQ      region  total_population  percent_white  \\\n",
       "0     alabama  336.504839     alabama           4799277             67   \n",
       "1      alaska  302.972581      alaska            720316             63   \n",
       "2     arizona  297.782903     arizona           6479703             57   \n",
       "3    arkansas  341.705806    arkansas           2933369             74   \n",
       "4  california  243.602258  california          37659181             40   \n",
       "\n",
       "   percent_black  percent_asian  percent_hispanic  per_capita_income  \\\n",
       "0             26              1                 4              23680   \n",
       "1              3              5                 6              32651   \n",
       "2              4              3                30              25358   \n",
       "3             15              1                 7              22170   \n",
       "4              6             13                38              29527   \n",
       "\n",
       "   median_rent  median_age  \n",
       "0          501        38.1  \n",
       "1          978        33.6  \n",
       "2          747        36.3  \n",
       "3          480        37.5  \n",
       "4         1119        35.4  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_for_model.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Now, creating train and test dataset\n",
    "criterion = weed_jan2015_summarized[\"State\"].map(lambda x: x in test_states)\n",
    "#Another way to do it\n",
    "#criterion = weed_jan2015_summarized.State.isin(test_states)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data labels: \n",
      "0      True\n",
      "1      True\n",
      "2      True\n",
      "3      True\n",
      "4      True\n",
      "5      True\n",
      "6      True\n",
      "7      True\n",
      "8      True\n",
      "9      True\n",
      "10     True\n",
      "11     True\n",
      "12    False\n",
      "13     True\n",
      "14     True\n",
      "15    False\n",
      "16     True\n",
      "17    False\n",
      "18     True\n",
      "19     True\n",
      "20     True\n",
      "21     True\n",
      "22    False\n",
      "23     True\n",
      "24     True\n",
      "25    False\n",
      "26     True\n",
      "27     True\n",
      "28    False\n",
      "29     True\n",
      "30    False\n",
      "31     True\n",
      "32     True\n",
      "33     True\n",
      "34     True\n",
      "35     True\n",
      "36     True\n",
      "37     True\n",
      "38     True\n",
      "39     True\n",
      "40     True\n",
      "41    False\n",
      "42     True\n",
      "43     True\n",
      "44     True\n",
      "45     True\n",
      "46     True\n",
      "47     True\n",
      "48     True\n",
      "49     True\n",
      "50    False\n",
      "Name: State, dtype: bool \n",
      "\n",
      "Test data labels: \n",
      "0     False\n",
      "1     False\n",
      "2     False\n",
      "3     False\n",
      "4     False\n",
      "5     False\n",
      "6     False\n",
      "7     False\n",
      "8     False\n",
      "9     False\n",
      "10    False\n",
      "11    False\n",
      "12     True\n",
      "13    False\n",
      "14    False\n",
      "15     True\n",
      "16    False\n",
      "17     True\n",
      "18    False\n",
      "19    False\n",
      "20    False\n",
      "21    False\n",
      "22     True\n",
      "23    False\n",
      "24    False\n",
      "25     True\n",
      "26    False\n",
      "27    False\n",
      "28     True\n",
      "29    False\n",
      "30     True\n",
      "31    False\n",
      "32    False\n",
      "33    False\n",
      "34    False\n",
      "35    False\n",
      "36    False\n",
      "37    False\n",
      "38    False\n",
      "39    False\n",
      "40    False\n",
      "41     True\n",
      "42    False\n",
      "43    False\n",
      "44    False\n",
      "45    False\n",
      "46    False\n",
      "47    False\n",
      "48    False\n",
      "49    False\n",
      "50     True\n",
      "Name: State, dtype: bool\n"
     ]
    }
   ],
   "source": [
    "print \"Train data labels: \\n\", ~criterion, \"\\n\\n\", \"Test data labels: \\n\", criterion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train = data_for_model[~criterion]\n",
    "test = data_for_model[criterion]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((42, 11), (9, 11))"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>HighQ</th>\n",
       "      <th>region</th>\n",
       "      <th>total_population</th>\n",
       "      <th>percent_white</th>\n",
       "      <th>percent_black</th>\n",
       "      <th>percent_asian</th>\n",
       "      <th>percent_hispanic</th>\n",
       "      <th>per_capita_income</th>\n",
       "      <th>median_rent</th>\n",
       "      <th>median_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>alabama</td>\n",
       "      <td>336.504839</td>\n",
       "      <td>alabama</td>\n",
       "      <td>4799277</td>\n",
       "      <td>67</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>23680</td>\n",
       "      <td>501</td>\n",
       "      <td>38.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>alaska</td>\n",
       "      <td>302.972581</td>\n",
       "      <td>alaska</td>\n",
       "      <td>720316</td>\n",
       "      <td>63</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>32651</td>\n",
       "      <td>978</td>\n",
       "      <td>33.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>arizona</td>\n",
       "      <td>297.782903</td>\n",
       "      <td>arizona</td>\n",
       "      <td>6479703</td>\n",
       "      <td>57</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>25358</td>\n",
       "      <td>747</td>\n",
       "      <td>36.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>arkansas</td>\n",
       "      <td>341.705806</td>\n",
       "      <td>arkansas</td>\n",
       "      <td>2933369</td>\n",
       "      <td>74</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>22170</td>\n",
       "      <td>480</td>\n",
       "      <td>37.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>california</td>\n",
       "      <td>243.602258</td>\n",
       "      <td>california</td>\n",
       "      <td>37659181</td>\n",
       "      <td>40</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>38</td>\n",
       "      <td>29527</td>\n",
       "      <td>1119</td>\n",
       "      <td>35.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>colorado</td>\n",
       "      <td>243.273226</td>\n",
       "      <td>colorado</td>\n",
       "      <td>5119329</td>\n",
       "      <td>70</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>31109</td>\n",
       "      <td>825</td>\n",
       "      <td>36.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>connecticut</td>\n",
       "      <td>336.442258</td>\n",
       "      <td>connecticut</td>\n",
       "      <td>3583561</td>\n",
       "      <td>70</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>37892</td>\n",
       "      <td>880</td>\n",
       "      <td>40.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>delaware</td>\n",
       "      <td>363.908387</td>\n",
       "      <td>delaware</td>\n",
       "      <td>908446</td>\n",
       "      <td>65</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>29819</td>\n",
       "      <td>828</td>\n",
       "      <td>38.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>district of columbia</td>\n",
       "      <td>348.107097</td>\n",
       "      <td>district of columbia</td>\n",
       "      <td>619371</td>\n",
       "      <td>35</td>\n",
       "      <td>49</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>45290</td>\n",
       "      <td>1154</td>\n",
       "      <td>33.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>florida</td>\n",
       "      <td>300.604194</td>\n",
       "      <td>florida</td>\n",
       "      <td>19091156</td>\n",
       "      <td>57</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>26236</td>\n",
       "      <td>838</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>georgia</td>\n",
       "      <td>323.562581</td>\n",
       "      <td>georgia</td>\n",
       "      <td>9810417</td>\n",
       "      <td>55</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>25182</td>\n",
       "      <td>673</td>\n",
       "      <td>35.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>hawaii</td>\n",
       "      <td>306.979677</td>\n",
       "      <td>hawaii</td>\n",
       "      <td>1376298</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>9</td>\n",
       "      <td>29305</td>\n",
       "      <td>1220</td>\n",
       "      <td>38.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>illinois</td>\n",
       "      <td>353.133226</td>\n",
       "      <td>illinois</td>\n",
       "      <td>12848554</td>\n",
       "      <td>63</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>29666</td>\n",
       "      <td>759</td>\n",
       "      <td>36.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>indiana</td>\n",
       "      <td>332.059677</td>\n",
       "      <td>indiana</td>\n",
       "      <td>6514861</td>\n",
       "      <td>81</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>24635</td>\n",
       "      <td>577</td>\n",
       "      <td>37.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>kansas</td>\n",
       "      <td>349.559032</td>\n",
       "      <td>kansas</td>\n",
       "      <td>2868107</td>\n",
       "      <td>78</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>26929</td>\n",
       "      <td>551</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>louisiana</td>\n",
       "      <td>361.621290</td>\n",
       "      <td>louisiana</td>\n",
       "      <td>4567968</td>\n",
       "      <td>60</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>24442</td>\n",
       "      <td>610</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>maine</td>\n",
       "      <td>310.723548</td>\n",
       "      <td>maine</td>\n",
       "      <td>1328320</td>\n",
       "      <td>94</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>26824</td>\n",
       "      <td>664</td>\n",
       "      <td>43.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>maryland</td>\n",
       "      <td>364.671935</td>\n",
       "      <td>maryland</td>\n",
       "      <td>5834299</td>\n",
       "      <td>54</td>\n",
       "      <td>29</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>36354</td>\n",
       "      <td>1034</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>massachusetts</td>\n",
       "      <td>344.880323</td>\n",
       "      <td>massachusetts</td>\n",
       "      <td>6605058</td>\n",
       "      <td>76</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>35763</td>\n",
       "      <td>936</td>\n",
       "      <td>39.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>minnesota</td>\n",
       "      <td>349.167097</td>\n",
       "      <td>minnesota</td>\n",
       "      <td>5347740</td>\n",
       "      <td>83</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>30913</td>\n",
       "      <td>734</td>\n",
       "      <td>37.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>mississippi</td>\n",
       "      <td>336.288065</td>\n",
       "      <td>mississippi</td>\n",
       "      <td>2976872</td>\n",
       "      <td>58</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>20618</td>\n",
       "      <td>510</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>montana</td>\n",
       "      <td>265.517097</td>\n",
       "      <td>montana</td>\n",
       "      <td>998554</td>\n",
       "      <td>87</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>25373</td>\n",
       "      <td>577</td>\n",
       "      <td>39.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>nebraska</td>\n",
       "      <td>335.264839</td>\n",
       "      <td>nebraska</td>\n",
       "      <td>1841625</td>\n",
       "      <td>82</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>26899</td>\n",
       "      <td>563</td>\n",
       "      <td>36.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>new hampshire</td>\n",
       "      <td>353.643548</td>\n",
       "      <td>new hampshire</td>\n",
       "      <td>1319171</td>\n",
       "      <td>92</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>33134</td>\n",
       "      <td>878</td>\n",
       "      <td>41.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>new mexico</td>\n",
       "      <td>288.175161</td>\n",
       "      <td>new mexico</td>\n",
       "      <td>2069706</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>23763</td>\n",
       "      <td>635</td>\n",
       "      <td>36.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>new york</td>\n",
       "      <td>343.555161</td>\n",
       "      <td>new york</td>\n",
       "      <td>19487053</td>\n",
       "      <td>58</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>32382</td>\n",
       "      <td>963</td>\n",
       "      <td>38.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>north carolina</td>\n",
       "      <td>344.903871</td>\n",
       "      <td>north carolina</td>\n",
       "      <td>9651380</td>\n",
       "      <td>65</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>25284</td>\n",
       "      <td>602</td>\n",
       "      <td>37.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>north dakota</td>\n",
       "      <td>392.652581</td>\n",
       "      <td>north dakota</td>\n",
       "      <td>689781</td>\n",
       "      <td>88</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>29732</td>\n",
       "      <td>564</td>\n",
       "      <td>36.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>ohio</td>\n",
       "      <td>332.440323</td>\n",
       "      <td>ohio</td>\n",
       "      <td>11549590</td>\n",
       "      <td>81</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>26046</td>\n",
       "      <td>562</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>oklahoma</td>\n",
       "      <td>353.359677</td>\n",
       "      <td>oklahoma</td>\n",
       "      <td>3785742</td>\n",
       "      <td>68</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>24208</td>\n",
       "      <td>525</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>oregon</td>\n",
       "      <td>205.483226</td>\n",
       "      <td>oregon</td>\n",
       "      <td>3868721</td>\n",
       "      <td>78</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>26809</td>\n",
       "      <td>749</td>\n",
       "      <td>38.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>pennsylvania</td>\n",
       "      <td>362.544194</td>\n",
       "      <td>pennsylvania</td>\n",
       "      <td>12731381</td>\n",
       "      <td>79</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>28502</td>\n",
       "      <td>652</td>\n",
       "      <td>40.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>rhode island</td>\n",
       "      <td>317.085806</td>\n",
       "      <td>rhode island</td>\n",
       "      <td>1051695</td>\n",
       "      <td>76</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>30469</td>\n",
       "      <td>781</td>\n",
       "      <td>39.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>south carolina</td>\n",
       "      <td>337.197419</td>\n",
       "      <td>south carolina</td>\n",
       "      <td>4679602</td>\n",
       "      <td>64</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>23943</td>\n",
       "      <td>582</td>\n",
       "      <td>38.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>tennessee</td>\n",
       "      <td>347.931613</td>\n",
       "      <td>tennessee</td>\n",
       "      <td>6402387</td>\n",
       "      <td>75</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>24409</td>\n",
       "      <td>568</td>\n",
       "      <td>38.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>texas</td>\n",
       "      <td>333.814194</td>\n",
       "      <td>texas</td>\n",
       "      <td>25639373</td>\n",
       "      <td>45</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>38</td>\n",
       "      <td>26019</td>\n",
       "      <td>688</td>\n",
       "      <td>33.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>utah</td>\n",
       "      <td>289.862258</td>\n",
       "      <td>utah</td>\n",
       "      <td>2813673</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>23873</td>\n",
       "      <td>739</td>\n",
       "      <td>29.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>vermont</td>\n",
       "      <td>370.526452</td>\n",
       "      <td>vermont</td>\n",
       "      <td>625904</td>\n",
       "      <td>94</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>29167</td>\n",
       "      <td>754</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>virginia</td>\n",
       "      <td>364.769032</td>\n",
       "      <td>virginia</td>\n",
       "      <td>8100653</td>\n",
       "      <td>64</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>33493</td>\n",
       "      <td>910</td>\n",
       "      <td>37.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>washington</td>\n",
       "      <td>233.028387</td>\n",
       "      <td>washington</td>\n",
       "      <td>6819579</td>\n",
       "      <td>72</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>30742</td>\n",
       "      <td>853</td>\n",
       "      <td>37.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>west virginia</td>\n",
       "      <td>358.037742</td>\n",
       "      <td>west virginia</td>\n",
       "      <td>1853619</td>\n",
       "      <td>93</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>22966</td>\n",
       "      <td>448</td>\n",
       "      <td>41.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>wisconsin</td>\n",
       "      <td>349.942903</td>\n",
       "      <td>wisconsin</td>\n",
       "      <td>5706871</td>\n",
       "      <td>83</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>27523</td>\n",
       "      <td>636</td>\n",
       "      <td>38.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   State       HighQ                region  total_population  \\\n",
       "0                alabama  336.504839               alabama           4799277   \n",
       "1                 alaska  302.972581                alaska            720316   \n",
       "2                arizona  297.782903               arizona           6479703   \n",
       "3               arkansas  341.705806              arkansas           2933369   \n",
       "4             california  243.602258            california          37659181   \n",
       "5               colorado  243.273226              colorado           5119329   \n",
       "6            connecticut  336.442258           connecticut           3583561   \n",
       "7               delaware  363.908387              delaware            908446   \n",
       "8   district of columbia  348.107097  district of columbia            619371   \n",
       "9                florida  300.604194               florida          19091156   \n",
       "10               georgia  323.562581               georgia           9810417   \n",
       "11                hawaii  306.979677                hawaii           1376298   \n",
       "13              illinois  353.133226              illinois          12848554   \n",
       "14               indiana  332.059677               indiana           6514861   \n",
       "16                kansas  349.559032                kansas           2868107   \n",
       "18             louisiana  361.621290             louisiana           4567968   \n",
       "19                 maine  310.723548                 maine           1328320   \n",
       "20              maryland  364.671935              maryland           5834299   \n",
       "21         massachusetts  344.880323         massachusetts           6605058   \n",
       "23             minnesota  349.167097             minnesota           5347740   \n",
       "24           mississippi  336.288065           mississippi           2976872   \n",
       "26               montana  265.517097               montana            998554   \n",
       "27              nebraska  335.264839              nebraska           1841625   \n",
       "29         new hampshire  353.643548         new hampshire           1319171   \n",
       "31            new mexico  288.175161            new mexico           2069706   \n",
       "32              new york  343.555161              new york          19487053   \n",
       "33        north carolina  344.903871        north carolina           9651380   \n",
       "34          north dakota  392.652581          north dakota            689781   \n",
       "35                  ohio  332.440323                  ohio          11549590   \n",
       "36              oklahoma  353.359677              oklahoma           3785742   \n",
       "37                oregon  205.483226                oregon           3868721   \n",
       "38          pennsylvania  362.544194          pennsylvania          12731381   \n",
       "39          rhode island  317.085806          rhode island           1051695   \n",
       "40        south carolina  337.197419        south carolina           4679602   \n",
       "42             tennessee  347.931613             tennessee           6402387   \n",
       "43                 texas  333.814194                 texas          25639373   \n",
       "44                  utah  289.862258                  utah           2813673   \n",
       "45               vermont  370.526452               vermont            625904   \n",
       "46              virginia  364.769032              virginia           8100653   \n",
       "47            washington  233.028387            washington           6819579   \n",
       "48         west virginia  358.037742         west virginia           1853619   \n",
       "49             wisconsin  349.942903             wisconsin           5706871   \n",
       "\n",
       "    percent_white  percent_black  percent_asian  percent_hispanic  \\\n",
       "0              67             26              1                 4   \n",
       "1              63              3              5                 6   \n",
       "2              57              4              3                30   \n",
       "3              74             15              1                 7   \n",
       "4              40              6             13                38   \n",
       "5              70              4              3                21   \n",
       "6              70              9              4                14   \n",
       "7              65             21              3                 8   \n",
       "8              35             49              3                10   \n",
       "9              57             15              2                23   \n",
       "10             55             30              3                 9   \n",
       "11             23              2             37                 9   \n",
       "13             63             14              5                16   \n",
       "14             81              9              2                 6   \n",
       "16             78              6              2                11   \n",
       "18             60             32              2                 4   \n",
       "19             94              1              1                 1   \n",
       "20             54             29              6                 8   \n",
       "21             76              6              6                10   \n",
       "23             83              5              4                 5   \n",
       "24             58             37              1                 3   \n",
       "26             87              0              1                 3   \n",
       "27             82              4              2                 9   \n",
       "29             92              1              2                 3   \n",
       "31             40              2              1                47   \n",
       "32             58             14              8                18   \n",
       "33             65             21              2                 9   \n",
       "34             88              1              1                 2   \n",
       "35             81             12              2                 3   \n",
       "36             68              7              2                 9   \n",
       "37             78              2              4                12   \n",
       "38             79             10              3                 6   \n",
       "39             76              5              3                13   \n",
       "40             64             28              1                 5   \n",
       "42             75             17              1                 5   \n",
       "43             45             12              4                38   \n",
       "44             80              1              2                13   \n",
       "45             94              1              1                 2   \n",
       "46             64             19              6                 8   \n",
       "47             72              3              7                11   \n",
       "48             93              3              1                 1   \n",
       "49             83              6              2                 6   \n",
       "\n",
       "    per_capita_income  median_rent  median_age  \n",
       "0               23680          501        38.1  \n",
       "1               32651          978        33.6  \n",
       "2               25358          747        36.3  \n",
       "3               22170          480        37.5  \n",
       "4               29527         1119        35.4  \n",
       "5               31109          825        36.1  \n",
       "6               37892          880        40.2  \n",
       "7               29819          828        38.9  \n",
       "8               45290         1154        33.8  \n",
       "9               26236          838        41.0  \n",
       "10              25182          673        35.6  \n",
       "11              29305         1220        38.3  \n",
       "13              29666          759        36.8  \n",
       "14              24635          577        37.1  \n",
       "16              26929          551        36.0  \n",
       "18              24442          610        36.0  \n",
       "19              26824          664        43.2  \n",
       "20              36354         1034        38.0  \n",
       "21              35763          936        39.2  \n",
       "23              30913          734        37.6  \n",
       "24              20618          510        36.2  \n",
       "26              25373          577        39.9  \n",
       "27              26899          563        36.3  \n",
       "29              33134          878        41.5  \n",
       "31              23763          635        36.7  \n",
       "32              32382          963        38.1  \n",
       "33              25284          602        37.6  \n",
       "34              29732          564        36.4  \n",
       "35              26046          562        39.0  \n",
       "36              24208          525        36.2  \n",
       "37              26809          749        38.7  \n",
       "38              28502          652        40.3  \n",
       "39              30469          781        39.6  \n",
       "40              23943          582        38.1  \n",
       "42              24409          568        38.2  \n",
       "43              26019          688        33.8  \n",
       "44              23873          739        29.6  \n",
       "45              29167          754        42.0  \n",
       "46              33493          910        37.5  \n",
       "47              30742          853        37.3  \n",
       "48              22966          448        41.5  \n",
       "49              27523          636        38.7  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>HighQ</th>\n",
       "      <th>region</th>\n",
       "      <th>total_population</th>\n",
       "      <th>percent_white</th>\n",
       "      <th>percent_black</th>\n",
       "      <th>percent_asian</th>\n",
       "      <th>percent_hispanic</th>\n",
       "      <th>per_capita_income</th>\n",
       "      <th>median_rent</th>\n",
       "      <th>median_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>idaho</td>\n",
       "      <td>274.990323</td>\n",
       "      <td>idaho</td>\n",
       "      <td>1583364</td>\n",
       "      <td>84</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>22568</td>\n",
       "      <td>607</td>\n",
       "      <td>34.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>iowa</td>\n",
       "      <td>364.967742</td>\n",
       "      <td>iowa</td>\n",
       "      <td>3062553</td>\n",
       "      <td>88</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>27027</td>\n",
       "      <td>534</td>\n",
       "      <td>38.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>kentucky</td>\n",
       "      <td>339.014516</td>\n",
       "      <td>kentucky</td>\n",
       "      <td>4361333</td>\n",
       "      <td>86</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>23462</td>\n",
       "      <td>506</td>\n",
       "      <td>38.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>michigan</td>\n",
       "      <td>287.620000</td>\n",
       "      <td>michigan</td>\n",
       "      <td>9886095</td>\n",
       "      <td>76</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>25681</td>\n",
       "      <td>623</td>\n",
       "      <td>39.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>missouri</td>\n",
       "      <td>351.059355</td>\n",
       "      <td>missouri</td>\n",
       "      <td>6007182</td>\n",
       "      <td>81</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>25649</td>\n",
       "      <td>549</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>nevada</td>\n",
       "      <td>265.610968</td>\n",
       "      <td>nevada</td>\n",
       "      <td>2730066</td>\n",
       "      <td>53</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>27</td>\n",
       "      <td>26589</td>\n",
       "      <td>840</td>\n",
       "      <td>36.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>new jersey</td>\n",
       "      <td>345.144839</td>\n",
       "      <td>new jersey</td>\n",
       "      <td>8832406</td>\n",
       "      <td>59</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "      <td>36027</td>\n",
       "      <td>1024</td>\n",
       "      <td>39.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>south dakota</td>\n",
       "      <td>357.954516</td>\n",
       "      <td>south dakota</td>\n",
       "      <td>825198</td>\n",
       "      <td>84</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>25740</td>\n",
       "      <td>517</td>\n",
       "      <td>36.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>wyoming</td>\n",
       "      <td>320.450968</td>\n",
       "      <td>wyoming</td>\n",
       "      <td>570134</td>\n",
       "      <td>85</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>28902</td>\n",
       "      <td>647</td>\n",
       "      <td>36.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           State       HighQ        region  total_population  percent_white  \\\n",
       "12         idaho  274.990323         idaho           1583364             84   \n",
       "15          iowa  364.967742          iowa           3062553             88   \n",
       "17      kentucky  339.014516      kentucky           4361333             86   \n",
       "22      michigan  287.620000      michigan           9886095             76   \n",
       "25      missouri  351.059355      missouri           6007182             81   \n",
       "28        nevada  265.610968        nevada           2730066             53   \n",
       "30    new jersey  345.144839    new jersey           8832406             59   \n",
       "41  south dakota  357.954516  south dakota            825198             84   \n",
       "50       wyoming  320.450968       wyoming            570134             85   \n",
       "\n",
       "    percent_black  percent_asian  percent_hispanic  per_capita_income  \\\n",
       "12              1              1                11              22568   \n",
       "15              3              2                 5              27027   \n",
       "17              8              1                 3              23462   \n",
       "22             14              3                 5              25681   \n",
       "25             11              2                 4              25649   \n",
       "28              8              7                27              26589   \n",
       "30             13              9                18              36027   \n",
       "41              1              1                 3              25740   \n",
       "50              1              1                 9              28902   \n",
       "\n",
       "    median_rent  median_age  \n",
       "12          607        34.9  \n",
       "15          534        38.1  \n",
       "17          506        38.2  \n",
       "22          623        39.1  \n",
       "25          549        38.0  \n",
       "28          840        36.6  \n",
       "30         1024        39.1  \n",
       "41          517        36.9  \n",
       "50          647        36.8  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear regression \n",
    "\n",
    "Let's use `statsmodels` for this workshop. \n",
    "\n",
    "Linear regression is of the form:\n",
    "\n",
    "$y = \\beta_0 + \\beta_1x_1 + \\beta_2x_2 + ... + \\beta_nx_n$\n",
    "\n",
    "- $y$ is what we have the predict/independent variable/response variable\n",
    "- $\\beta_0$ is the intercept/slope\n",
    "- $\\beta_1$ is the coefficient for $x_1$ (the first feature/dependent variable)\n",
    "- $\\beta_n$ is the coefficient for $x_n$ (the nth feature/dependent variable)\n",
    "\n",
    "The $\\beta$ are called *model coefficients*\n",
    "\n",
    "The model coefficients are estimated in this process. (In Machine Learning parlance - the weights are learned using the algorithm). The objective function is least squares method. \n",
    "<br>\n",
    "\n",
    "**Least Squares Method** : To identify the weights so that the overall solution minimizes the sum of the squares of the errors made in the results of every single equation. [Wiki](https://en.wikipedia.org/wiki/Least_squares)\n",
    "\n",
    "![Estimating coefficients](img/leastsquare.gif)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import seaborn as sns\n",
    "sns.set(color_codes = True)\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.rcParams['figure.figsize'] = (8, 6)\n",
    "plt.rcParams['font.size'] = 14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#First step: Let's visualize bivariate plots with  a trend line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10c01fd90>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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byVv1bwKuBR6MMbaAVgjhu8BVwMEBl02SpMob6Bh9jPEvyLvju74E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\nhRn0kiRVmEEvSVKFGfSSJFXY/w/nZGf7nIT32QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108847450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x=\"percent_white\", y = \"HighQ\", data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10c20af50>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LK50bS2XVgCd6VAM6XSaY3bZ/sqpqwB7U8zp5a+0jwC8N6FiE3k1vssV3gAJ+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\nRURECkohLyIiUlD/P7N02HSjRsSjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bc50390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x=\"total_population\", y=\"HighQ\", data=train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise** Plot for `HighQ` vs `per_capita_income` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Fitting another kind of plot/model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "?sns.lmplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x10d79d490>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FLLOVeqd3ZcPWMk1LRKQjBkt5om18B1oFrIh0rTAMKOa37w0SFbAi0tUGStlt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\n/a4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d70b090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x=\"total_population\", y=\"HighQ\", data=train, order=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10d7dd090>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WJo95MDMzs5a4eDAzM7NW9PMkUaUY+O/AzMzMSuWWh4qYP21K7gi9OnCbz+aO0NST112f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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d74cc10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#More plots.\n",
    "\n",
    "#Visualizing correlation matrix using a heatmap\n",
    "\n",
    "sns.heatmap(train.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "?sns.pairplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x10c324590>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BDqAdiJe4rtEU8JRPvQC76Od2IAZMoY5vztMGTC7HWF9f2+VftIrtuGnLzTGV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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c324610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Multiple scatter plot\n",
    "\n",
    "sns.pairplot(train, x_vars='HighQ', y_vars=['total_population', 'percent_white', 'percent_black', \\\n",
    "                                           'per_capita_income', 'median_rent', 'median_age'], kind='reg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**First, let's build a single variable model.**\n",
    "\n",
    "Let's try to estimate price as a function of population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "feature_columns = [\"total_population\"]\n",
    "train_x = train[feature_columns]\n",
    "train_y = train['HighQ']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_1 = LinearRegression()\n",
    "model_1.fit(train_x, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "333.662079418\n",
      "[ -1.04692952e-06]\n"
     ]
    }
   ],
   "source": [
    "print model_1.intercept_\n",
    "print model_1.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let's use the model for prediction**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_1_predict = model_1.predict(test[feature_columns])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model_1_predict = pd.DataFrame({'States': test.State, 'Actual Price': test.HighQ, 'Predicted Price': model_1_predict})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Actual Price</th>\n",
       "      <th>Predicted Price</th>\n",
       "      <th>States</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>274.990323</td>\n",
       "      <td>332.004409</td>\n",
       "      <td>idaho</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>364.967742</td>\n",
       "      <td>330.455802</td>\n",
       "      <td>iowa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>339.014516</td>\n",
       "      <td>329.096071</td>\n",
       "      <td>kentucky</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>287.620000</td>\n",
       "      <td>323.312035</td>\n",
       "      <td>michigan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>351.059355</td>\n",
       "      <td>327.372983</td>\n",
       "      <td>missouri</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>265.610968</td>\n",
       "      <td>330.803893</td>\n",
       "      <td>nevada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>345.144839</td>\n",
       "      <td>324.415173</td>\n",
       "      <td>new jersey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>357.954516</td>\n",
       "      <td>332.798155</td>\n",
       "      <td>south dakota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>320.450968</td>\n",
       "      <td>333.065189</td>\n",
       "      <td>wyoming</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Actual Price  Predicted Price        States\n",
       "12    274.990323       332.004409         idaho\n",
       "15    364.967742       330.455802          iowa\n",
       "17    339.014516       329.096071      kentucky\n",
       "22    287.620000       323.312035      michigan\n",
       "25    351.059355       327.372983      missouri\n",
       "28    265.610968       330.803893        nevada\n",
       "30    345.144839       324.415173    new jersey\n",
       "41    357.954516       332.798155  south dakota\n",
       "50    320.450968       333.065189       wyoming"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_1_predict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Computing mean squared error**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36.281030710323748"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Root Mean square error on test dataset\n",
    "np.sqrt(np.mean(np.square(model_1_predict['Actual Price'] - \\\n",
    "                         model_1_predict['Predicted Price'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Lower the RMSE, better the model **"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise: Create model using `total_population` and `per_capita_income` as the features. Report RMSE**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pointers\n",
    "\n",
    "To build the model the right way, the following need to be done. They are left as exercise. \n",
    "\n",
    "1. Scale the features\n",
    "2. Use cross-validation\n",
    "3. As more features are added, the model becomes complicated and would overfit the data. Will need regularization\n",
    "4. Try feature transformation to see if lower RMSE is possible. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Why wasn't **R-square** and **p-value** covered? \n",
    "\n",
    "- Linear models rely upon a lot of assumptions ([here](http://andrewgelman.com/2013/08/04/19470/)). If assumptions are violated, the diagnostics obtained from the model cannot be relied. \n",
    "- Biggest challenge is that adding any feature will increase the R-square. One way to counter this is to use adjusted R-squre.\n",
    "- Take a step back and think - why do we need to report those numbers? We want some estimate of generalization. Cross-validation score provides a general framework for reporting generalization. And this will hold good across all models. And thus, multiple models can be compared. This is the machine learning approach and is widely used in practice."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bonus: R-square and p-value\n",
    "\n",
    "This won't be covered in the worksop, but the code is left here, for experimentation later on.\n",
    "<br>\n",
    "This would be done using `statsmodels`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import statsmodels.formula.api as smf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept           333.662079\n",
       "total_population     -0.000001\n",
       "dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create the model\n",
    "lm_model_1 = smf.ols(formula='HighQ ~ total_population', data=train).fit()\n",
    "\n",
    "#Coefficients of the model\n",
    "lm_model_1.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 332.00440891,  330.45580229,  329.09607117,  323.31203475,\n",
       "        327.37298327,  330.80389274,  324.41517287,  332.79815527,\n",
       "        333.0651893 ])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm_model_1.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>316.876244</td>\n",
       "      <td>3.504479e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_population</th>\n",
       "      <td>-0.000003</td>\n",
       "      <td>6.656067e-07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           0             1\n",
       "Intercept         316.876244  3.504479e+02\n",
       "total_population   -0.000003  6.656067e-07"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#95% Confidence interval of the model\n",
    "lm_model_1.conf_int()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise: Find the 90% CI **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept           5.942308e-34\n",
       "total_population    2.238336e-01\n",
       "dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# p-value of the model coefficients\n",
    "lm_model_1.pvalues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.036761583666067521"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#R-squared for the model\n",
    "lm_model_1.rsquared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>HighQ</td>      <th>  R-squared:         </th> <td>   0.037</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.013</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   1.527</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 02 Oct 2015</td> <th>  Prob (F-statistic):</th>  <td> 0.224</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>22:15:55</td>     <th>  Log-Likelihood:    </th> <td> -213.72</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    42</td>      <th>  AIC:               </th> <td>   431.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    40</td>      <th>  BIC:               </th> <td>   434.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>        <td>  333.6621</td> <td>    8.305</td> <td>   40.174</td> <td> 0.000</td> <td>  316.876   350.448</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>total_population</th> <td>-1.047e-06</td> <td> 8.47e-07</td> <td>   -1.236</td> <td> 0.224</td> <td>-2.76e-06  6.66e-07</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>14.136</td> <th>  Durbin-Watson:     </th> <td>   2.338</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.001</td> <th>  Jarque-Bera (JB):  </th> <td>  14.943</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-1.303</td> <th>  Prob(JB):          </th> <td>0.000569</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.323</td> <th>  Cond. No.          </th> <td>1.31e+07</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  HighQ   R-squared:                       0.037\n",
       "Model:                            OLS   Adj. R-squared:                  0.013\n",
       "Method:                 Least Squares   F-statistic:                     1.527\n",
       "Date:                Fri, 02 Oct 2015   Prob (F-statistic):              0.224\n",
       "Time:                        22:15:55   Log-Likelihood:                -213.72\n",
       "No. Observations:                  42   AIC:                             431.4\n",
       "Df Residuals:                      40   BIC:                             434.9\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "====================================================================================\n",
       "                       coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
       "------------------------------------------------------------------------------------\n",
       "Intercept          333.6621      8.305     40.174      0.000       316.876   350.448\n",
       "total_population -1.047e-06   8.47e-07     -1.236      0.224     -2.76e-06  6.66e-07\n",
       "==============================================================================\n",
       "Omnibus:                       14.136   Durbin-Watson:                   2.338\n",
       "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               14.943\n",
       "Skew:                          -1.303   Prob(JB):                     0.000569\n",
       "Kurtosis:                       4.323   Cond. No.                     1.31e+07\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.31e+07. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Summary of fitted model\n",
    "lm_model_1.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Another model with total_population and per_capita_income\n",
    "lm_model_2 = smf.ols(formula='HighQ ~ total_population + per_capita_income', data=train).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>HighQ</td>      <th>  R-squared:         </th> <td>   0.047</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>  -0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>  0.9559</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 02 Oct 2015</td> <th>  Prob (F-statistic):</th>  <td> 0.393</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>22:16:00</td>     <th>  Log-Likelihood:    </th> <td> -213.50</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    42</td>      <th>  AIC:               </th> <td>   433.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    39</td>      <th>  BIC:               </th> <td>   438.2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>             <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>         <td>  309.7736</td> <td>   38.330</td> <td>    8.082</td> <td> 0.000</td> <td>  232.244   387.304</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>total_population</th>  <td>-1.034e-06</td> <td> 8.54e-07</td> <td>   -1.211</td> <td> 0.233</td> <td>-2.76e-06  6.93e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>per_capita_income</th> <td>    0.0008</td> <td>    0.001</td> <td>    0.639</td> <td> 0.527</td> <td>   -0.002     0.004</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>14.699</td> <th>  Durbin-Watson:     </th> <td>   2.341</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.001</td> <th>  Jarque-Bera (JB):  </th> <td>  15.822</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-1.335</td> <th>  Prob(JB):          </th> <td>0.000367</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.383</td> <th>  Cond. No.          </th> <td>6.01e+07</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  HighQ   R-squared:                       0.047\n",
       "Model:                            OLS   Adj. R-squared:                 -0.002\n",
       "Method:                 Least Squares   F-statistic:                    0.9559\n",
       "Date:                Fri, 02 Oct 2015   Prob (F-statistic):              0.393\n",
       "Time:                        22:16:00   Log-Likelihood:                -213.50\n",
       "No. Observations:                  42   AIC:                             433.0\n",
       "Df Residuals:                      39   BIC:                             438.2\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=====================================================================================\n",
       "                        coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
       "-------------------------------------------------------------------------------------\n",
       "Intercept           309.7736     38.330      8.082      0.000       232.244   387.304\n",
       "total_population  -1.034e-06   8.54e-07     -1.211      0.233     -2.76e-06  6.93e-07\n",
       "per_capita_income     0.0008      0.001      0.639      0.527        -0.002     0.004\n",
       "==============================================================================\n",
       "Omnibus:                       14.699   Durbin-Watson:                   2.341\n",
       "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               15.822\n",
       "Skew:                          -1.335   Prob(JB):                     0.000367\n",
       "Kurtosis:                       4.383   Cond. No.                     6.01e+07\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 6.01e+07. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm_model_2.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Is model_2 better than model_1 ? **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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